generators

Data Generators

Data generators (also known as data driven/parametrized test cases) let you reuse the same set of assertions across different input values. In Catch2, this means that they respect the ordering and nesting of the TEST_CASE and SECTION macros, and their nested sections are run once per each value in a generator.

This is best explained with an example:

TEST_CASE("Generators") {
    auto i = GENERATE(1, 2, 3);
    SECTION("one") {
        auto j = GENERATE( -3, -2, -1 );
        REQUIRE(j < i);
    }
}

The assertion in this test case will be run 9 times, because there are 3 possible values for i (1, 2, and 3) and there are 3 possible values for j (-3, -2, and -1).

There are 2 parts to generators in Catch2, the GENERATE macro together with the already provided generators, and the IGenerator<T> interface that allows users to implement their own generators.

Provided generators

Catch2's provided generator functionality consists of three parts,

• GENERATE macro, that serves to integrate generator expression with a test case,
• 2 fundamental generators
  • ValueGenerator<T> – contains only single element
  • ValuesGenerator<T> – contains multiple elements
• 5 generic generators that modify other generators
  • FilterGenerator<T, Predicate> – filters out elements from a generator for which the predicate returns "false"
  • TakeGenerator<T> – takes first n elements from a generator
  • RepeatGenerator<T> – repeats output from a generator n times
  • MapGenerator<T, U, Func> – returns the result of applying Func on elements from a different generator
  • ChunkGenerator<T> – returns chunks (inside std::vector) of n elements from a generator
• 3 specific purpose generators
  • RandomIntegerGenerator<Integral> – generates random Integrals from range
  • RandomFloatGenerator<Float> – generates random Floats from range
  • RangeGenerator<T> – generates all values inside a specific range

The generators also have associated helper functions that infer their type, making their usage much nicer. These are

• value(T&&) for ValueGenerator<T>
• values(std::initializer_list<T>) for ValuesGenerator<T>
• filter(predicate, GeneratorWrapper<T>&&) for FilterGenerator<T, Predicate>
• take(count, GeneratorWrapper<T>&&) for TakeGenerator<T>
• repeat(repeats, GeneratorWrapper<T>&&) for RepeatGenerator<T>
• map(func, GeneratorWrapper<T>&&) for MapGenerator<T, U, Func> (map U to T, deduced from Func)
• map<T>(func, GeneratorWrapper<U>&&) for MapGenerator<T, U, Func> (map U to T)
• chunk(chunk-size, GeneratorWrapper<T>&&) for ChunkGenerator<T>
• random(IntegerOrFloat a, IntegerOrFloat b) for RandomIntegerGenerator or RandomFloatGenerator
• range(start, end) for RangeGenerator<T> with a step size of 1
• range(start, end, step) for RangeGenerator<T> with a custom step size

And can be used as shown in the example below to create a generator that returns 100 odd random number:

TEST_CASE("Generating random ints", "[example][generator]") {
    SECTION("Deducing functions") {
        auto i = GENERATE(take(100, filter([](int i) { return i % 2 == 1; }, random(-100, 100))));
        REQUIRE(i > -100);
        REQUIRE(i < 100);
        REQUIRE(i % 2 == 1);
    }
}

Apart from registering generators with Catch2, the GENERATE macro has one more purpose, and that is to provide simple way of generating trivial generators, as seen in the first example on this page, where we used it as auto i = GENERATE(1, 2, 3);. This usage converted each of the three literals into a single ValueGenerator<int> and then placed them all in a special generator that concatenates other generators. It can also be used with other generators as arguments, such as auto i = GENERATE(0, 2, take(100, random(300, 3000)));. This is useful e.g. if you know that specific inputs are problematic and want to test them separately/first.

For safety reasons, you cannot use variables inside the GENERATE macro. This is done because the generator expression will outlive the outside scope and thus capturing references is dangerous. If you need to use variables inside the generator expression, make sure you thought through the lifetime implications and use GENERATE_COPY or GENERATE_REF.

You can also override the inferred type by using as<type> as the first argument to the macro. This can be useful when dealing with string literals, if you want them to come out as std::string:

TEST_CASE("type conversion", "[generators]") {
    auto str = GENERATE(as<std::string>{}, "a", "bb", "ccc");
    REQUIRE(str.size() > 0);
}

Generator interface

You can also implement your own generators, by deriving from the IGenerator<T> interface:

template<typename T>
struct IGenerator : GeneratorUntypedBase {
    // via GeneratorUntypedBase:
    // Attempts to move the generator to the next element.
    // Returns true if successful (and thus has another element that can be read)
    virtual bool next() = 0;
 
    // Precondition:
    // The generator is either freshly constructed or the last call to next() returned true
    virtual T const& get() const = 0;
};

However, to be able to use your custom generator inside GENERATE, it will need to be wrapped inside a GeneratorWrapper<T>. GeneratorWrapper<T> is a value wrapper around a std::unique_ptr<IGenerator<T>>.

For full example of implementing your own generator, look into Catch2's examples, specifically Generators: Create your own generator.